Merchant Store Number Accuracy via Transaction Data Centroid Analysis
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Solution Overview
Problem
Inconsistent naming conventions and human errors in merchant identification lead to incorrect payee identification numbers in electronic payment systems, complicating transaction analysis and rewards provisioning for multi-store merchants.
Innovation Solution
A computer-implemented method and system that uses machine learning and artificial intelligence to parse and clean transaction data, identifying centroids for accurate merchant store information and correcting errors in merchant store numbers, thereby ensuring accurate merchant store number data for each transaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If merchants manually manage merchant identification at each terminal, then merchants can control their own identification data, but inconsistent naming conventions and human errors result in incorrect payee identification numbers
Solution Approach 1:
The patent introduces a backend system as an intermediary that automatically assigns payee identification numbers based on merchant identification and store number. This mediator prevents direct human error from propagating to payee identification, while still allowing merchants to manage their terminal data locally. The backend system acts as the bridge between merchant-managed identification and system-wide payee identification assignment.
Solution Approach 2:
The system implements feedback mechanisms where the backend analyzes transaction data to detect inconsistent naming conventions and corrects payee identification number assignments. By continuously monitoring transaction patterns and merchant identification data, the system can identify errors and adjust assignments to maintain accuracy, creating a self-correcting loop that improves reliability over time.
2Productivity
If the backend assigns a different payee identification for each merchant identification, then each transaction can be processed individually, but transaction analysis for each store and merchant becomes difficult and based on incorrect data
Solution Approach 1:
The patent applies preliminary action by having the backend system pre-analyze and validate merchant identification data before assigning payee identification numbers. By checking for consistent naming conventions and verifying store number accuracy in advance, the system prevents incorrect data from entering the transaction processing stream, ensuring both processing efficiency and data accuracy for subsequent analysis.
3Adaptability or versatility
If multiple electronic payment terminals are deployed at each location, then merchants can serve multiple customers simultaneously, but equipment changes and human error increase inconsistent naming conventions
Solution Approach 1:
The patent implements universality by creating a standardized merchant identification structure that works across multiple terminals and locations. The system uses a universal format combining merchant identification with store number that can be consistently applied regardless of how many terminals a merchant operates. This universal approach allows multi-terminal deployment while maintaining data consistency through standardized naming conventions enforced by the backend system.
Data Source
AI summary
A system and method may retrieve transaction data for a plurality of electronic payment system transactions. The transaction data may describe a plurality of electronic payment transactions between a payment network system and a plurality of merchants. The transaction data may include a transaction location and a merchant store number. The system and method may parse the transaction data to identify the transaction location and the merchant store number and analyze the retrieved and parsed transaction data to identify one or more centroids for at least a portion of the retrieved and parsed transaction data. The one or more centroids may correspond to a merchant location for each transaction of the retrieved and parsed transaction data. The system and method may then clean the retrieved and parsed transaction data having a transaction location outside a threshold distance from the one or more centroids to include accurate merchant store number data.